BayesFBHborrow: An R Package for Bayesian borrowing for time-to-event data from a flexible baseline hazard

TL;DR

This paper introduces BayesFBHborrow, an R package for Bayesian borrowing in time-to-event data, allowing flexible baseline hazard modeling and improved borrowing with non-exchangeable historical data.

Contribution

It proposes a novel Bayesian semiparametric model with smoothing priors and lump-and-smear borrowing priors, implemented in an accessible R package.

Findings

Demonstrated on simulated data showing accurate baseline hazard estimation.

Applied to real-world data illustrating effective borrowing and error control.

Enhanced model estimation and borrowing flexibility in time-to-event analysis.

Abstract

There is currently a focus on statistical methods which can use external trial information to help accelerate the discovery, development and delivery of medicine. Bayesian methods facilitate borrowing which is "dynamic" in the sense that the similarity of the data helps to determine how much information is used. We propose a Bayesian semiparameteric model, which allows the baseline hazard to take any form through an ensemble average. We introduce priors to smooth the posterior baseline hazard improving both model estimation and borrowing characteristics. A "lump-and-smear" borrowing prior accounts for non-exchangable historical data and helps reduce the maximum type I error in the presence of prior-data conflict. In this article, we present BayesFBHborrow, an R package, which enables the user to perform Bayesian borrowing with a historical control dataset in a semiparameteric time-to-event model. User-defined hyperparameters smooth an ensemble averaged posterior baseline hazard. The model offers the specification of lump-and-smear priors on the commensurability parameter where the associated hyperparameters can be chosen according to the users tolerance for difference between the log baseline hazards. We demonstrate the performance of our Bayesian flexible baseline hazard model on a simulated and real world dataset.